Information processing method based on neural network, neural network and training method thereof
The invention provides an information processing method based on a neural network, the neural network and a training method thereof, and relates to the field of artificial intelligence, in particular to a machine learning technology and a deep learning technology. The method comprises the following...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | GUO ZHUONING QIN CHUAN XIONG HUI LIU HAO ZHANG LE ZHU HENGSHU |
| description | The invention provides an information processing method based on a neural network, the neural network and a training method thereof, and relates to the field of artificial intelligence, in particular to a machine learning technology and a deep learning technology. The method comprises the following steps: determining company characteristics of a target company and position characteristics of a target position; at least one demand value corresponding to at least one timestamp included in the target time sequence is processed to obtain demand time sequence features, and each demand value indicates the talent demand degree of the target company for the target position at the corresponding timestamp; at least one supply value corresponding to the at least one timestamp is processed to obtain supply time sequence features, and each supply value indicates the talent supply degree of the target company to the target position at the corresponding timestamp; and processing the company characteristics, the position cha |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115409479A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115409479A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115409479A3</originalsourceid><addsrcrecordid>eNrjZEj0zEvLL8pNLMnMz1MoKMpPTi0uzsxLV8hNLcnIT1FISixOTVEASuWllhYl5gCpkvL8omwdNL5CYl6KQklRYmYekt6SjNSi1Pw0HgbWtMSc4lReKM3NoOjmGuLsoZtakB-fWlyQmJwKNCXe2c_Q0NTEwNLE3NLRmBg1AFusPkM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Information processing method based on neural network, neural network and training method thereof</title><source>esp@cenet</source><creator>GUO ZHUONING ; QIN CHUAN ; XIONG HUI ; LIU HAO ; ZHANG LE ; ZHU HENGSHU</creator><creatorcontrib>GUO ZHUONING ; QIN CHUAN ; XIONG HUI ; LIU HAO ; ZHANG LE ; ZHU HENGSHU</creatorcontrib><description>The invention provides an information processing method based on a neural network, the neural network and a training method thereof, and relates to the field of artificial intelligence, in particular to a machine learning technology and a deep learning technology. The method comprises the following steps: determining company characteristics of a target company and position characteristics of a target position; at least one demand value corresponding to at least one timestamp included in the target time sequence is processed to obtain demand time sequence features, and each demand value indicates the talent demand degree of the target company for the target position at the corresponding timestamp; at least one supply value corresponding to the at least one timestamp is processed to obtain supply time sequence features, and each supply value indicates the talent supply degree of the target company to the target position at the corresponding timestamp; and processing the company characteristics, the position cha</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221129&DB=EPODOC&CC=CN&NR=115409479A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20221129&DB=EPODOC&CC=CN&NR=115409479A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>GUO ZHUONING</creatorcontrib><creatorcontrib>QIN CHUAN</creatorcontrib><creatorcontrib>XIONG HUI</creatorcontrib><creatorcontrib>LIU HAO</creatorcontrib><creatorcontrib>ZHANG LE</creatorcontrib><creatorcontrib>ZHU HENGSHU</creatorcontrib><title>Information processing method based on neural network, neural network and training method thereof</title><description>The invention provides an information processing method based on a neural network, the neural network and a training method thereof, and relates to the field of artificial intelligence, in particular to a machine learning technology and a deep learning technology. The method comprises the following steps: determining company characteristics of a target company and position characteristics of a target position; at least one demand value corresponding to at least one timestamp included in the target time sequence is processed to obtain demand time sequence features, and each demand value indicates the talent demand degree of the target company for the target position at the corresponding timestamp; at least one supply value corresponding to the at least one timestamp is processed to obtain supply time sequence features, and each supply value indicates the talent supply degree of the target company to the target position at the corresponding timestamp; and processing the company characteristics, the position cha</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZEj0zEvLL8pNLMnMz1MoKMpPTi0uzsxLV8hNLcnIT1FISixOTVEASuWllhYl5gCpkvL8omwdNL5CYl6KQklRYmYekt6SjNSi1Pw0HgbWtMSc4lReKM3NoOjmGuLsoZtakB-fWlyQmJwKNCXe2c_Q0NTEwNLE3NLRmBg1AFusPkM</recordid><startdate>20221129</startdate><enddate>20221129</enddate><creator>GUO ZHUONING</creator><creator>QIN CHUAN</creator><creator>XIONG HUI</creator><creator>LIU HAO</creator><creator>ZHANG LE</creator><creator>ZHU HENGSHU</creator><scope>EVB</scope></search><sort><creationdate>20221129</creationdate><title>Information processing method based on neural network, neural network and training method thereof</title><author>GUO ZHUONING ; QIN CHUAN ; XIONG HUI ; LIU HAO ; ZHANG LE ; ZHU HENGSHU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115409479A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>GUO ZHUONING</creatorcontrib><creatorcontrib>QIN CHUAN</creatorcontrib><creatorcontrib>XIONG HUI</creatorcontrib><creatorcontrib>LIU HAO</creatorcontrib><creatorcontrib>ZHANG LE</creatorcontrib><creatorcontrib>ZHU HENGSHU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>GUO ZHUONING</au><au>QIN CHUAN</au><au>XIONG HUI</au><au>LIU HAO</au><au>ZHANG LE</au><au>ZHU HENGSHU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Information processing method based on neural network, neural network and training method thereof</title><date>2022-11-29</date><risdate>2022</risdate><abstract>The invention provides an information processing method based on a neural network, the neural network and a training method thereof, and relates to the field of artificial intelligence, in particular to a machine learning technology and a deep learning technology. The method comprises the following steps: determining company characteristics of a target company and position characteristics of a target position; at least one demand value corresponding to at least one timestamp included in the target time sequence is processed to obtain demand time sequence features, and each demand value indicates the talent demand degree of the target company for the target position at the corresponding timestamp; at least one supply value corresponding to the at least one timestamp is processed to obtain supply time sequence features, and each supply value indicates the talent supply degree of the target company to the target position at the corresponding timestamp; and processing the company characteristics, the position cha</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN115409479A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Information processing method based on neural network, neural network and training method thereof |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T08%3A29%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=GUO%20ZHUONING&rft.date=2022-11-29&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115409479A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |